Motivation: As alpha-helical transmembrane proteins constitute roughly
25% of a typical genome and are vital parts of many essential biological
processes, structural knowledge of these proteins is necessary for
increasing our understanding of such processes. Because structural
knowledge of transmembrane proteins is difficult to attain experimentally,
improved methods for prediction of structural features of these
proteins is important.

Functionality: OCTOPUS uses a combination
of hidden Markov models and artificial neural networks.
In particular,
OCTOPUS is the first topology predictor to integrate
modeling of reentrant-, membrane dip-, and TM hairpin regions in the topological grammar.
OCTOPUS first performs a homology search using BLAST to create a
sequence profile. This is used as the input to a set of neural networks
that predict both the preference for each residue to be located
in a transmembrane (M), interface (I), close loop (L) or
globular loop (G) environment and the preference for each residue to
be on the inside (i) or outside (o) if the membrane.
In the third step, these predictions are used as input to a two-track hidden
Markov model, which uses them to calculate the most likely topology.